### Modeling risk to aquatic species subject to realistic, dynamic exposures using a generalized form of Haber’s law Dr. Rick Reiss
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### Introduction 

### • Current exposure models in salmon BiOps estimate instantaneous concentration in streams 

### • These exposure estimates are compared to toxicity tests conducted over 48 to 96 hours with constant exposure.

– Mismatch in exposure profile with streams 2

### Salmonid Habitat 

### • Salmon generally reside in flowing water bodies

– Fast dispersion of spray drift 

### • Backwater pools, dammed pools and beaver ponds may represent 20% of capacity

– Still expect quicker loss than in standard ecotoxicity tests 3

### Dye Added to Small, Low Flow Stream

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### Concentration vs. Time: Static Pond versus Stream

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

0 10 20 30 40 50 Concentration (ppm)
Time (hours) Static Pond Stream

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### Simplified Example – C*T Loading Scenario Initial

Concentration Dissipation Time C*T Ecotoxicity Test 1 ppm None 48 hours 48 ppm - hour Static Pond 1 ppm None 48 hours 48 ppm - hour Stream 1 ppm T 1/2 = 3 hours 48 hours 5 ppm - hour

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### Estimating Stream Concentrations with AGDRIFT

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### Assumed Range of Stream Velocities and Stream Depths 

### • Reviewed limited data on salmonid bearing streams 

### • Juvenile rearing Chinook salmon prefer slower-velocity streambank areas (USFWS, 1986):

– Migrate an average of 1.6 km/day

– Optimal stream depths of 0.3 - 1.2 m

– Optimal stream velocities of 0.06 -0.24 m/sec 8

### Assumed Range of Stream Velocities and Stream Depths 

### • Assumed stream depths of 0.1 to 0.5 meters 

### • Assumed stream velocities of 0.02 -0.5 m/sec

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### Other Modeling Assumptions 

### • Aerial application with rate of 0.5 lb/A 

### • 100 feet upwind from stream (runoff assumed negligible) 

### • Fine-to- medium sized droplets 

### • Riparian interception factor of 0.2 

### • Wind direction directly from field to water body 

### • No dissipation or degradation 10

### Time vs. Concentration: Averaging Period Has Dramatic Impact

0 5 10 15 20 25 30 35 40 45 50

0 12 24 36 48 Concentration (ppb)

Hour Depth = 0.1 m Velocity = 0.02 m/sec

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### Accounting for Different Toxicity from Different Concentration Profiles Haber’s Law: L=C*T 

### • Most risk assessments assume that this is true and that we can compare a time weighted average concentration to a toxicity level. 

### • Stream dilution may present an extreme case 12

### Generalized Haber’s Law Generalized Haber’s Law: where n is the toxic load exponent. If n>1, a short pulse exposure has a greater effect than constant exposure with same C*T. 13

### Generalized Haber’s Law Consider an exponential decline in concentration,

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### Example Using Generalized Model

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### Estimating Toxic Load Exponent Data from Legierse et al. (1999) 

### • 14- day lethality to guppies or pond snails 

### • Six OP insecticides 

### • n varied from 0.91 - 1.5 for five pesticides and was 2.7 for methidathion for 2 -4 day exposures
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### Toxic load exponent fits for 11 OPs and 2 non - OP pesticides

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### Putting It All Together: Modeling Results

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### AGDRIFT Results Fitted to General Equations

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### Distribution of Fitted Rate Constants

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### Dilution Ratios 

### • Stream dispersion will create effective dilution ratios of 50 -300

– Even after accounting for n>1 toxic load exponents 

### • Results may be conservative

– Salmonids move though different water depths to regulate body temperature
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### Recommendations 

### • Use AGDRIFT Stream Assessment Tool or equations in our paper to estimate stream concentrations 

### • Approach could be refined with:

– More steam characteristic data

– Inclusion of more observation points in routine ecotoxicological testing

– Probability of having different habitat characteristics 22

### Publication 

### • In press* at Risk Analysis: An International Journal Bogen KT; Reiss, R. Generalized Haber’s law for exponential concentration decline, with application to riparian -aquatic pesticide ecotoxicity. Risk Anal 2011 (in press). DOI: 10.1111/j.1539- 6924.2011.01663.x .
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### Acknowledgments 

### • Cheminova provided partial funding for this work as part of its comments on the salmonid BiOps for malathion and dimethoate

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